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About

D. Salas-Gonzalez is a leading researcher at the intersection of artificial intelligence and biomedical data analysis, with a primary focus on computational approaches to Explainable Artificial Intelligence (XAI) and deep learning applications in medical imaging. Their most cited work, "Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends" (2023), has garnered 191 citations, establishing them as a key voice in making complex AI systems transparent and interpretable. This research addresses the critical challenge of understanding how deep learning models—which excel at extracting high-level features from complex data—arrive at their decisions, a necessity for clinical adoption. Beyond XAI, Salas-Gonzalez has made significant contributions to the analysis of functional neuroimaging data, particularly in Alzheimer's disease research using PET and MRI scans. Their work often bridges advanced machine learning techniques with practical medical diagnostics, demonstrating how non-linear artificial neural systems can improve disease detection and classification. By combining theoretical advances in explainability with applied biomedical solutions, Salas-Gonzalez's research is shaping a future where AI is not only powerful but also trustworthy and interpretable for critical healthcare decisions.